{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import gzip\n",
    "\n",
    "test_file = 'test.gz'\n",
    "samplesubmision_file = 'sampleSubmission.gz'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Chunk No. 1\n",
      "Processing Chunk No. 2\n",
      "Processing Chunk No. 3\n",
      "Processing Chunk No. 4\n",
      "Processing Chunk No. 5\n",
      "Processing Chunk No. 6\n",
      "Processing Chunk No. 7\n",
      "Processing Chunk No. 8\n",
      "Processing Chunk No. 9\n",
      "Processing Chunk No. 10\n",
      "Processing Chunk No. 11\n",
      "Processing Chunk No. 12\n",
      "Processing Chunk No. 13\n",
      "Processing Chunk No. 14\n",
      "Processing Chunk No. 15\n",
      "Processing Chunk No. 16\n",
      "Processing Chunk No. 17\n",
      "Processing Chunk No. 18\n",
      "Processing Chunk No. 19\n",
      "Processing Chunk No. 20\n",
      "Processing Chunk No. 21\n",
      "Processing Chunk No. 22\n",
      "Processing Chunk No. 23\n",
      "Processing Chunk No. 24\n",
      "Processing Chunk No. 25\n",
      "Processing Chunk No. 26\n",
      "Processing Chunk No. 27\n",
      "Processing Chunk No. 28\n",
      "Processing Chunk No. 29\n",
      "Processing Chunk No. 30\n",
      "Processing Chunk No. 31\n",
      "Processing Chunk No. 32\n",
      "Processing Chunk No. 33\n",
      "Processing Chunk No. 34\n",
      "Processing Chunk No. 35\n",
      "Processing Chunk No. 36\n",
      "Processing Chunk No. 37\n",
      "Processing Chunk No. 38\n",
      "Processing Chunk No. 39\n",
      "Processing Chunk No. 40\n",
      "Processing Chunk No. 41\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021448"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunksize = 10 ** 6\n",
    "num_of_chunk = 0\n",
    "train = pd.DataFrame()\n",
    "    \n",
    "for chunk in pd.read_csv('train.csv', chunksize=chunksize):\n",
    "    num_of_chunk += 1\n",
    "    train = pd.concat([train, chunk.sample(frac=.05, replace=False, random_state=123)], axis=0)\n",
    "    print('Processing Chunk No. ' + str(num_of_chunk))     \n",
    "    \n",
    "train.reset_index(inplace=True)\n",
    "\n",
    "# Backup data train length, df for later use re-partitioned index\n",
    "train_len = len(train)\n",
    "train_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train, pd.read_csv(test_file, compression='gzip')]).drop(['index', 'id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to convert hour data into date format\n",
    "def get_date(hour):\n",
    "    y = '20'+str(hour)[:2]\n",
    "    m = str(hour)[2:4]\n",
    "    d = str(hour)[4:6]\n",
    "    return y+'-'+m+'-'+d\n",
    "\n",
    "# Create a weekday field and fill in the hour after conversion\n",
    "df['weekday'] = pd.to_datetime(df.hour.apply(get_date)).dt.dayofweek.astype(str)\n",
    "\n",
    "# Create a function to convert hour data into time period\n",
    "def tran_hour(x):\n",
    "    x = x % 100\n",
    "    while x in [23,0]:\n",
    "        return '23-01'\n",
    "    while x in [1,2]:\n",
    "        return '01-03'\n",
    "    while x in [3,4]:\n",
    "        return '03-05'\n",
    "    while x in [5,6]:\n",
    "        return '05-07'\n",
    "    while x in [7,8]:\n",
    "        return '07-09'\n",
    "    while x in [9,10]:\n",
    "        return '09-11'\n",
    "    while x in [11,12]:\n",
    "        return '11-13'\n",
    "    while x in [13,14]:\n",
    "        return '13-15'\n",
    "    while x in [15,16]:\n",
    "        return '15-17'\n",
    "    while x in [17,18]:\n",
    "        return '17-19'\n",
    "    while x in [19,20]:\n",
    "        return '19-21'\n",
    "    while x in [21,22]:\n",
    "        return '21-23'\n",
    "\n",
    "# Convert hour to time period\n",
    "df['hour'] = df.hour.apply(tran_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6598912 entries, 0 to 4577463\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Dtype  \n",
      "---  ------            -----  \n",
      " 0   click             float64\n",
      " 1   hour              object \n",
      " 2   C1                int64  \n",
      " 3   banner_pos        int64  \n",
      " 4   site_id           object \n",
      " 5   site_domain       object \n",
      " 6   site_category     object \n",
      " 7   app_id            object \n",
      " 8   app_domain        object \n",
      " 9   app_category      object \n",
      " 10  device_id         object \n",
      " 11  device_ip         object \n",
      " 12  device_model      object \n",
      " 13  device_type       int64  \n",
      " 14  device_conn_type  int64  \n",
      " 15  C14               int64  \n",
      " 16  C15               int64  \n",
      " 17  C16               int64  \n",
      " 18  C17               int64  \n",
      " 19  C18               int64  \n",
      " 20  C19               int64  \n",
      " 21  C20               int64  \n",
      " 22  C21               int64  \n",
      " 23  weekday           object \n",
      "dtypes: float64(1), int64(12), object(11)\n",
      "memory usage: 1.2+ GB\n"
     ]
    }
   ],
   "source": [
    "# Confirmation document type by\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>hour</th>\n",
       "      <th>C1</th>\n",
       "      <th>banner_pos</th>\n",
       "      <th>site_id</th>\n",
       "      <th>site_domain</th>\n",
       "      <th>site_category</th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_domain</th>\n",
       "      <th>app_category</th>\n",
       "      <th>...</th>\n",
       "      <th>device_conn_type</th>\n",
       "      <th>C14</th>\n",
       "      <th>C15</th>\n",
       "      <th>C16</th>\n",
       "      <th>C17</th>\n",
       "      <th>C18</th>\n",
       "      <th>C19</th>\n",
       "      <th>C20</th>\n",
       "      <th>C21</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>01-03</td>\n",
       "      <td>1005</td>\n",
       "      <td>1</td>\n",
       "      <td>d9750ee7</td>\n",
       "      <td>98572c79</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>17753</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>1063</td>\n",
       "      <td>-1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>01-03</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
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       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>07d7df22</td>\n",
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       "      <td>0</td>\n",
       "      <td>15699</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100083</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>01-03</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
       "      <td>f3845767</td>\n",
       "      <td>28905ebd</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>07d7df22</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>15703</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>100083</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>03-05</td>\n",
       "      <td>1005</td>\n",
       "      <td>1</td>\n",
       "      <td>b8eae5f9</td>\n",
       "      <td>1e334bd3</td>\n",
       "      <td>f028772b</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>07d7df22</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>19950</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1800</td>\n",
       "      <td>3</td>\n",
       "      <td>167</td>\n",
       "      <td>100077</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>01-03</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>1fbe01fe</td>\n",
       "      <td>f3845767</td>\n",
       "      <td>28905ebd</td>\n",
       "      <td>ecad2386</td>\n",
       "      <td>7801e8d9</td>\n",
       "      <td>07d7df22</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>15701</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>1722</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>-1</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   click   hour    C1  banner_pos   site_id site_domain site_category  \\\n",
       "0    1.0  01-03  1005           1  d9750ee7    98572c79      f028772b   \n",
       "1    0.0  01-03  1005           0  1fbe01fe    f3845767      28905ebd   \n",
       "2    0.0  01-03  1005           0  1fbe01fe    f3845767      28905ebd   \n",
       "3    0.0  03-05  1005           1  b8eae5f9    1e334bd3      f028772b   \n",
       "4    0.0  01-03  1005           0  1fbe01fe    f3845767      28905ebd   \n",
       "\n",
       "     app_id app_domain app_category  ... device_conn_type    C14  C15  C16  \\\n",
       "0  ecad2386   7801e8d9     07d7df22  ...                0  17753  320   50   \n",
       "1  ecad2386   7801e8d9     07d7df22  ...                0  15699  320   50   \n",
       "2  ecad2386   7801e8d9     07d7df22  ...                0  15703  320   50   \n",
       "3  ecad2386   7801e8d9     07d7df22  ...                0  19950  320   50   \n",
       "4  ecad2386   7801e8d9     07d7df22  ...                0  15701  320   50   \n",
       "\n",
       "    C17  C18   C19     C20  C21  weekday  \n",
       "0  1993    2  1063      -1   33        1  \n",
       "1  1722    0    35  100083   79        1  \n",
       "2  1722    0    35  100083   79        1  \n",
       "3  1800    3   167  100077   23        1  \n",
       "4  1722    0    35      -1   79        1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After confirming the value count of each feature, it was found that the feature of type int, the feature with the most value is only 4,333 value counts, and a data set of 6 million data is obviously a non-continuous row variable. It can be concluded that all the features of this data set are variables of Object type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C1 : 7\n",
      "banner_pos : 7\n",
      "site_id : 3496\n",
      "site_domain : 4333\n",
      "site_category : 24\n",
      "app_id : 5466\n",
      "app_domain : 319\n",
      "app_category : 31\n",
      "device_id : 552856\n",
      "device_ip : 1875405\n",
      "device_model : 6321\n",
      "device_type : 5\n",
      "device_conn_type : 4\n",
      "C14 : 2662\n",
      "C15 : 8\n",
      "C16 : 9\n",
      "C17 : 470\n",
      "C18 : 4\n",
      "C19 : 68\n",
      "C20 : 169\n",
      "C21 : 62\n"
     ]
    }
   ],
   "source": [
    "len_of_feature_count = []\n",
    "for i in df.columns[2:23].tolist():\n",
    "    print(i, ':', len(df[i].astype(str).value_counts()))\n",
    "    len_of_feature_count.append(len(df[i].astype(str).value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list and save the feature names of the lines that need to be converted into the list\n",
    "need_tran_feature = df.columns[2:4].tolist() + df.columns[13:23].tolist()\n",
    "\n",
    "# Convert variables to object type in sequence\n",
    "for i in need_tran_feature:\n",
    "    df[i] = df[i].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some features have extremely high value counts, even millions of data values. In this case, one-hot encoding will undoubtedly cause dimensionality collapse. Here, the value count of each feature is limited to 10, and once the value exceeds 10, the reduction operation will be performed.\n",
    "\n",
    "The method of reduction is to calculate the click-through rate of all values of a variable, and the click-through rate is divided into very_high, higher, mid, lower, very_low, etc. 5 levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['site_id',\n",
       " 'site_domain',\n",
       " 'site_category',\n",
       " 'app_id',\n",
       " 'app_domain',\n",
       " 'app_category',\n",
       " 'device_id',\n",
       " 'device_ip',\n",
       " 'device_model',\n",
       " 'C14',\n",
       " 'C17',\n",
       " 'C19',\n",
       " 'C20',\n",
       " 'C21']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_features = []\n",
    "\n",
    "for i in range(len(len_of_feature_count)):\n",
    "    if len_of_feature_count[i] > 10:\n",
    "        obj_features.append(df.columns[2:23].tolist()[i])\n",
    "obj_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.021448e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.700731e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.756971e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              click\n",
       "count  2.021448e+06\n",
       "mean   1.700731e-01\n",
       "std    3.756971e-01\n",
       "min    0.000000e+00\n",
       "25%    0.000000e+00\n",
       "50%    0.000000e+00\n",
       "75%    0.000000e+00\n",
       "max    1.000000e+00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_describe = df.describe()\n",
    "df_describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run:  site_id\n",
      "Run:  site_domain\n",
      "Run:  site_category\n",
      "Run:  app_id\n",
      "Run:  app_domain\n",
      "Run:  app_category\n",
      "Run:  device_id\n",
      "Run:  device_ip\n",
      "Run:  device_model\n",
      "Run:  C14\n",
      "Run:  C17\n",
      "Run:  C19\n",
      "Run:  C20\n",
      "Run:  C21\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click</th>\n",
       "      <th>hour</th>\n",
       "      <th>C1</th>\n",
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       "<p>6598912 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         click   hour    C1 banner_pos    site_id site_domain site_category  \\\n",
       "0          1.0  01-03  1005          1  very_high   very_high           mid   \n",
       "1          0.0  01-03  1005          0     higher      higher        higher   \n",
       "2          0.0  01-03  1005          0     higher      higher        higher   \n",
       "3          0.0  03-05  1005          1   very_low    very_low           mid   \n",
       "4          0.0  01-03  1005          0     higher      higher        higher   \n",
       "...        ...    ...   ...        ...        ...         ...           ...   \n",
       "4577459    NaN  23-01  1005          0  very_high   very_high     very_high   \n",
       "4577460    NaN  23-01  1005          0        mid      higher           mid   \n",
       "4577461    NaN  23-01  1005          0  very_high   very_high     very_high   \n",
       "4577462    NaN  23-01  1005          0   very_low    very_low      very_low   \n",
       "4577463    NaN  23-01  1005          0        mid      higher           mid   \n",
       "\n",
       "           app_id app_domain app_category  ... device_conn_type  C14  C15  \\\n",
       "0          higher     higher       higher  ...                0  mid  320   \n",
       "1          higher     higher       higher  ...                0  mid  320   \n",
       "2          higher     higher       higher  ...                0  mid  320   \n",
       "3          higher     higher       higher  ...                0  mid  320   \n",
       "4          higher     higher       higher  ...                0  mid  320   \n",
       "...           ...        ...          ...  ...              ...  ...  ...   \n",
       "4577459    higher     higher       higher  ...                0  mid  300   \n",
       "4577460    higher     higher       higher  ...                0  mid  320   \n",
       "4577461    higher     higher       higher  ...                0  mid  300   \n",
       "4577462  very_low   very_low     very_low  ...                3  mid  320   \n",
       "4577463    higher     higher       higher  ...                0  mid  320   \n",
       "\n",
       "         C16  C17 C18  C19  C20  C21 weekday  \n",
       "0         50  mid   2  mid  mid  mid       1  \n",
       "1         50  mid   0  mid  mid  mid       1  \n",
       "2         50  mid   0  mid  mid  mid       1  \n",
       "3         50  mid   3  mid  mid  mid       1  \n",
       "4         50  mid   0  mid  mid  mid       1  \n",
       "...      ...  ...  ..  ...  ...  ...     ...  \n",
       "4577459  250  mid   2  mid  mid  mid       4  \n",
       "4577460   50  mid   0  mid  mid  mid       4  \n",
       "4577461  250  mid   2  mid  mid  mid       4  \n",
       "4577462   50  mid   1  mid  mid  mid       4  \n",
       "4577463   50  mid   0  mid  mid  mid       4  \n",
       "\n",
       "[6598912 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def obj_clean(X):\n",
    "    # Define a function to reduce the data value, processing one feature vector at a time\n",
    "\n",
    "    def get_click_rate(x):\n",
    "        # Define a function to get the click rate\n",
    "        temp = train[train[X.columns[0]] == x]\n",
    "        res = round((temp.click.sum() / temp.click.count()),3)\n",
    "        return res\n",
    "\n",
    "    def get_type(V, str):\n",
    "        # Define a function to obtain the level distance judgment of the new data value\n",
    "        very_high = df_describe.loc['mean','click'] + 0.04\n",
    "        higher = df_describe.loc['mean','click'] + 0.02\n",
    "        lower = df_describe.loc['mean','click'] - 0.02\n",
    "        very_low = df_describe.loc['mean','click'] - 0.04\n",
    "\n",
    "        vh_type = V[V[str] > very_high].index.tolist()\n",
    "        hr_type = V[(V[str] > higher) & (V[str] < very_high)].index.tolist()\n",
    "        vl_type = V[V[str] < very_low].index.tolist()\n",
    "        lr_type = V[(V[str] < lower) & (V[str] > very_low)].index.tolist()\n",
    "\n",
    "        return vh_type, hr_type, vl_type, lr_type\n",
    "\n",
    "    def clean_function(x):\n",
    "        # Define a function to convert data values based on the level distance\n",
    "        # The basis for judgment is: the plus or minus 4% of the total average click-through rate is very_high(low), and the plus or minus 2% of the total average click-through rate is higher (lower)\n",
    "        while x in type_[0]:\n",
    "            return 'very_high'\n",
    "        while x in type_[1]:\n",
    "            return 'higher'\n",
    "        while x in type_[2]:\n",
    "            return 'very_low'\n",
    "        while x in type_[3]:\n",
    "            return 'lower'\n",
    "        return 'mid'\n",
    "        \n",
    "    print('Run: ', X.columns[0])\n",
    "    fq = X[X.columns[0]].value_counts()\n",
    "    # Create a temporary frequency list of data values\n",
    "    # Theoretically, all data values are classified and converted to get the best results; in practice, in order to perform time efficiency, the frequency of data values after the top 1000 rows will be dropped.\n",
    "    if len(fq) > 1000:\n",
    "        fq = fq[:1000]\n",
    "\n",
    "    # Convert the frequency list into a dataframe, and fill in the index in a new field.\n",
    "    fq = pd.DataFrame(fq)\n",
    "    fq['new_column'] = fq.index    \n",
    "\n",
    "    # Use the index to call the get_click_rate function to obtain the click rate of each data value\n",
    "    fq['click_rate'] = fq.new_column.apply(get_click_rate)\n",
    "\n",
    "   # Invoke the get_type function to obtain the classification distance, and save it as a list, so that it can be used by the next clean_function\n",
    "    type_ = get_type(fq, 'click_rate')\n",
    "\n",
    "   # Invoke clean_funtion funtion, return the converted feature vector\n",
    "    return X[X.columns[0]].apply(clean_function)\n",
    "\n",
    "# Use the for loop to input the features to be converted into the obj_clean function\n",
    "for i in obj_features:    \n",
    "    df[[i]] = obj_clean(df[[i]])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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m4D8w+iD7DUnuGPtksySvAL4ILKuqx5Mc21Q/kqTumpwRnAVsqapHq2oXcAtwwbiaDwC3VdXjAFW1tcF+JEldNBkEJwBPjNke6ewb61TgqCQ/THJ/khXd3ijJ5UmGkwxv27atoXYlqZ2aDIJ02Vfjtg8B3gi8CzgX+PMkp+71oqrVVTVUVUMDAwPT36kktVhj5wgYnQGcOGZ7EHiqS832qvoV8Ksk9wBLgIcb7EuSNEaTM4INwClJFieZD1wE3DGu5nbg95MckuRw4E3AQw32JEkap7EZQVXtTnIFcDcwD7i5qjYmWdkZX1VVDyW5C3gA+A3wpap6sKmeJEl7a/LQEFW1Hlg/bt+qcdufAT7TZB+SpIl5Z7EktZxBIEktZxBIUssZBJLUcgaBJLWcQSBJLWcQSFLLGQSS1HIGgSS1nEEgSS1nEEhSyxkEktRyBoEktZxBIEktZxBIUss1GgRJliXZnGRLkqsnqVuaZE+SC5vsR5K0t8aCIMk84CbgPOB04OIkp09Q9xeMPslMkjTDmpwRnAVsqapHq2oXcAtwQZe6K4GvA1sb7EWSNIEmg+AE4Ikx2yOdfS9KcgLwbuAlj68cL8nlSYaTDG/btm3aG5WkNmsyCNJlX43b/jzw8araM9kbVdXqqhqqqqGBgYHp6k+SRLMPrx8BThyzPQg8Na5mCLglCcBCYHmS3VX1zQb7kiSN0WQQbABOSbIYeBK4CPjA2IKqWvzC70nWAN82BCRpZjUWBFW1O8kVjF4NNA+4uao2JlnZGZ/0vIAkaWY0OSOgqtYD68ft6xoAVXVJk71IkrrzzmJJarlGZwSStL8ev/53+93CrPHKa37W6Ps7I5CkljMIJKnlDAJJajmDQJJaziCQpJYzCCSp5QwCSWo5g0CSWs4gkKSWMwgkqeUMAklqOYNAklrOIJCkljMIJKnlGg2CJMuSbE6yJcnVXcY/mOSBzs+9SZY02Y8kaW+NBUGSecBNwHnA6cDFSU4fV/YYcHZVnQl8CljdVD+SpO6anBGcBWypqkerahdwC3DB2IKqureqft7ZvA8YbLAfSVIXTQbBCcATY7ZHOvsm8hHgzm4DSS5PMpxkeNu2bdPYoiSpySBIl33VtTB5O6NB8PFu41W1uqqGqmpoYGBgGluUJDX5zOIR4MQx24PAU+OLkpwJfAk4r6p2NNiPJKmLJmcEG4BTkixOMh+4CLhjbEGSVwK3AR+qqocb7EWSNIHGZgRVtTvJFcDdwDzg5qramGRlZ3wVcA1wDPDFJAC7q2qoqZ4kSXtr8tAQVbUeWD9u36oxv18GXNZkD5KkyXlnsSS1nEEgSS1nEEhSyxkEktRyBoEktZxBIEktZxBIUssZBJLUcgaBJLWcQSBJLWcQSFLLGQSS1HIGgSS1nEEgSS1nEEhSyxkEktRyjQZBkmVJNifZkuTqLuNJckNn/IEkb2iyH0nS3hoLgiTzgJuA84DTgYuTnD6u7DzglM7P5cBfN9WPJKm7JmcEZwFbqurRqtoF3AJcMK7mAmBtjboPeEWS4xrsSZI0TpPPLD4BeGLM9gjwph5qTgCeHluU5HJGZwwAv0yyeXpbba989k8WAtv73YfUhd/NF1yb6XiXV0000GQQdOu89qOGqloNrJ6OpvRSSYaraqjffUjj+d2cOU0eGhoBThyzPQg8tR81kqQGNRkEG4BTkixOMh+4CLhjXM0dwIrO1UNvBv6lqp4e/0aSpOY0dmioqnYnuQK4G5gH3FxVG5Os7IyvAtYDy4EtwL8CH26qH03IQ26arfxuzpBU7XVIXpLUIt5ZLEktZxBIUssZBC3hch+ajZLcnGRrkgcnGPd7OQMMghZwuQ/NYmuAZZOM+72cAQZBO7jch2alqroH2DlJid/LGWAQtMNES3nsa4000/xezgCDoB2mbbkPaYb5vZwBBkE7uNyH5iq/lzPAIGgHl/vQXOX3cgY0ufqoZgmX+9BslWQdcA6wMMkIcC1wKPi9nEkuMSFJLeehIUlqOYNAklrOIJCkljMIJKnlDAJJajmDQNoPSa5L8qed369P8geT1F6S5MaZ607aN95HIB2gqrqm3z1IB8IZgdSDJCs66+H/NMnfjBtbk+TCzu9Lk9zbqftJkiPG1b4ryT8kWTiT/UuTcUYgTSHJGcAngH9fVduTHA381y5184FbgfdX1YYkRwK/HjP+buC/A8ur6ucz0700NYNAmto7gK9V1XaAqtqZdFsUk9OAp6tqQ6fuGYBO7duBIeAPX9gvzRYeGpKmFnpb+niyukeBI4BTp6spaboYBNLU/g54X5JjADqHhrr5X8DxSZZ26o5I8sKs+5+B9wBrO4eapFnDQ0PSFDortX4a+FGSPcD/BP6pS92uJO8HvpDkMEbPD/zBmPHNST4I/G2SP6qq/z0zn0CanKuPSlLLeWhIklrOIJCkljMIJKnlDAJJajmDQJJaziCQpJYzCCSp5f4/bzRLNVptT/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ubt/cdtt3AHfkWYeZmXXMvyw2M0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwSl2sQSJoiaYOkzZJu6qTfREn7JV2SZz1mZnaw3IJAUj9gDnAhMA6YJmlcB/2+CzyeVy1mZtaxPEcEk4DNEbElIvYCi4GLS/S7HngQ2JFjLWZm1oE8g2AksK1ouyXb9wFJI4EvAXM7O5CkmZKaJTXv3Lmz4oWamaUszyBQiX3Rbvv7wI0Rsb+zA0XEvIhojIjG+vr6StVnZmZA/xyP3QKMKtpuALa369MILJYEUAdMlbQvIn6aY11mZlYkzyBYDYyVNAZ4A7gc+MviDhExpu2+pAXAow4BM7PelVsQRMQ+SddRmA3UD5gfEeskzcraO/1ewMzMekeeIwIiogloarevZABExIw8azEzs9L8y2Izs8Q5CMzMEucgMDNLnIPAzCxxDgIzs8Q5CMzMEpfr9NGUvX7LGdUuAYDjZ79a7RLMrI/ziMDMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHEOAjOzxDkIzMwS5yAwM0ucg8DMLHG5BoGkKZI2SNos6aYS7VdIeiW7vSBpQp71mJnZwXILAkn9gDnAhcA4YJqkce26bQXOj4jxwK3AvLzqMTOz0vIcEUwCNkfElojYCywGLi7uEBEvRMRb2eZKoCHHeszMrIQ8g2AksK1ouyXb15GvAI+VapA0U1KzpOadO3dWsEQzM8szCFRiX5TsKH2aQhDcWKo9IuZFRGNENNbX11ewRDMzy/NSlS3AqKLtBmB7+06SxgP3ABdGRGuO9ZiZWQl5jghWA2MljZE0ELgcWFbcQdLxwEPAlRGxMcdazMysA7mNCCJin6TrgMeBfsD8iFgnaVbWPheYDQwH7pYEsC8iGvOqyczMDpbnqSEiogloardvbtH9a4Br8qzBzMw6518Wm5klzkFgZpa4XE8NWe07++sLq10CAGvumF7tEswOWx4RmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzrOGzHpRX5iF5RlY1p5HBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpY4B4GZWeIcBGZmiXMQmJklzkFgZpa4XNcakjQF+AHQD7gnIm5v166sfSrwO2BGRLyYZ01Wm16/5Yxql8Dxs1+tdglmuchtRCCpHzAHuBAYB0yTNK5dtwuBsdltJvDjvOoxM7PS8hwRTAI2R8QWAEmLgYuB9UV9LgYWRkQAKyUdLWlERLyZY11mSesLoyvwCKsvyTMIRgLbirZbgHPK6DMSOCAIJM2kMGIAeFfShsqWWnknQB2wq9p18G1Vu4KK6BPvp9/LyjpM3k/6yvvZtRM6asgzCEr9V44e9CEi5gHzKlFUb5HUHBGN1a7jcOH3s3L8XlbW4fB+5jlrqAUYVbTdAGzvQR8zM8tRnkGwGhgraYykgcDlwLJ2fZYB01VwLvAbfz9gZta7cjs1FBH7JF0HPE5h+uj8iFgnaVbWPhdoojB1dDOF6aNX5VVPFdTUqawa4PezcvxeVlbNv58qTNgxM7NU+ZfFZmaJcxCYmSXOQVBhkqZI2iBps6Sbql1PLZM0StIzkl6TtE7SDdWuqZZJGiRplaSXs/fzO9WuqdZJ+rWkVyW9JKm52vX0lL8jqKBsWY2NwOcoTI1dDUyLiPWdPtBKkjQCGBERL0oaCqwBvuj3s2eytb0+FBHvShoA/CtwQ0SsrHJpNUvSr4HGiKiFH5R1yCOCyvpgWY2I2Au0LathPRARb7YtQhgRe4DXKPzy3HogCt7NNgdkN/8laA6CCutoyQw7RJJGA2cB/1blUmqapH6SXgJ2AE9GhN/PQxPAE5LWZEvh1KRcl6FOUFlLZlj3SPow8CDwtxHxTrXrqWURsR84U9LRwFJJH42ItVUuq5Z9IiK2SzoWeFLSryJiRbWL6i6PCCrLS2ZUWHYu+0Hgvoh4qNr1HC4i4m3gWWBKdSupbRGxPft3B7CUwunhmuMgqKxyltWwMmVfbv4j8FpEfK/a9dQ6SfXZSABJg4HPAr+qalE1TNKHskkMSPoQ8HmgJkdXPjVUQR0tq1HlsmrZJ4ArgVez89oA34iIpuqVVNNGAP+UzW47Arg/Ih6tck217E8pnF6Dwmfpv0TE8uqW1DOePmpmljifGjIzS5yDwMwscQ4CM7PEOQjMzBLnIDAzS5yDwMwscQ4CqxmSbpb0tR487hZJn82jpt4i6RvVrsEOX/4dgdUMSTcD70bEndWupbdJejciPlztOuzw5BGB9WmSvpld6Ocp4NRs30mSlmcrPj4n6TRJR2UXCTki6zNE0jZJAyQtkHRJtn+ipBeyi7OskjQ0W5HzDkmrJb0i6W+6qOl/ZxcjeVnS7dm+MyWtzB6/VNIx2f5nJX03e66Nkj6Z7Z8h6aHsdWyS9PedPN/twODs4if3Sbq1+CI9km6T9L8kTZa0Inv+9ZLmFr0fn5f0S0kvSvpJtpCfWUFE+OZbn7wBZwOvAkOAI4HNwNeAnwNjsz7nAE9n9x8GPp3dvwy4J7u/ALgEGAhsASZm+4+ksDTATOBb2b4/AZqBMR3UdCHwAjAk2x6W/fsKcH52/xbg+9n9Z4F/yO5PBZ7K7s/IajkKGAT8OzCqk/fi3aL7o4EXs/tHAP8PGA5MBt4DTqSwxMmT2euuA1ZQuCgNwI3A7Gr/9/Wt79y81pD1ZZ8ElkbE7wAkLaPwoXke8JNsjRcofHgDLKEQAM9QWPDv7nbHOxV4MyJWA0S2pLWkzwPj20YNFD6cxwJbS9T0WeDetpoiYreko4CjI+IXWZ9/An5S9Ji2VVPXUPgQb/PziPhNVsN64AQOvJ5FSRHxa0mtks6isN7N/42I1uz9WBURW7JjLgL+G4VwGAc8n/UZCPyyq+exdDgIrK9r/yXWEcDbEXFmib7LgL+TNIzCaOLpdu0qcby2/ddHxONl1NPRMTrzh+zf/Rz4/9wfiu63b+vKPRRGFX8GzC/a3762oFDzkxExrRvHt4T4OwLry1YAX5I0OFvu9wvA74Ctki6FwlLVkiYAROEyjKuAHwCPRuEiLMV+BRwnaWL22KGS+lNYLfar2bUPkHRKtqxwKU8AV0sakvUdlv1V/1bb+X8KK6b+ooPH99T7bfVlllK4lsDErP42k7Jl0I+gMDr6V2Al8AlJJ2c1D5F0SoXrsxrmEYH1WVG4aP0S4CUK59Cfy5quAH4s6VsUrru7GHg5a1tC4bTM5BLH2yvpMuBH2Xr8v6dwqucesvPu2TUQdgJf7KCm5ZLOBJol7QWagG8Afw3MzQJiC3DVIbz0UuYBr0h6MSKuyF7LMxRGR8WB90vgduAMCkG6NCL+KGkGsEhS22m0bwEbK1yj1ShPHzWrQdlf/C8Cl0bEpmzfZOBrEfE/qlia1SCfGjKrMZLGUZhB9fO2EDA7FB4RmJUg6Qzgn9vt/kNEnJPz8/4b/zULqs2VEfFqns9raXMQmJklzqeGzMwS5yAwM0ucg8DMLHEOAjOzxP1/JnTXH1qHl8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CXkslHQR8FxgTEW9JOjSreszMrLQs1whGA6sj4vWI2ALMAc4vanMxMDci3gKIiHUZ1mNmZiVkGQSHAWsKhtvScYWGAwdLelbSYkkTSz2QpEmSWiS1rF+/PqNyzczyKcsgUIlxUTTcEzgZ+DxwLvB3kobvNFPE9IhoioimhoaG6ldqZpZjWV53uA0YVDDcCKwt0WZDRLwPvC/peWAksDLDuszMrECWawSLgGGShkrqDYwH5hW1eQT4Q0k9JfUFTgGWZ1iTmZkVyWyNICK2SZoCPA70AGZERKukyen0aRGxXNIC4FXgQ+CeiFiaVU1mZrazLDcNERHNQHPRuGlFw7cDt2dZh5mZleczi83Mcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyLtMgkDRG0gpJqyVN7aDdKEnbJV2QZT1mZrazzIJAUg/gbuA84FhggqRjy7T7Bsm1jc3MrMayXCMYDayOiNcjYgswBzi/RLtrgB8B6zKsxczMysgyCA4D1hQMt6XjPiLpMOALwA4XtC8maZKkFkkt69evr3qhZmZ5lmUQqMS4KBr+NnBDRGzv6IEiYnpENEVEU0NDQ7XqMzMzoGeGj90GDCoYbgTWFrVpAuZIAugPjJW0LSIezrAuMzMrkGUQLAKGSRoKvA2MBy4ubBARQ9vvS5oJPOYQMDOrrcyCICK2SZpCcjRQD2BGRLRKmpxO73C/gJmZ1UaWawRERDPQXDSuZABExGVZ1mJmZqX5zGIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMci7T8wi6urdu+XSn5x1802tVrMTMrH68RmBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzmQaBpDGSVkhaLWlqielfkvRqentR0sgs6zEzs51lFgSSegB3A+cBxwITJB1b1OwN4IyIGAHcCkzPqh4zMystyzWC0cDqiHg9IrYAc4DzCxtExIsR8at0cCHQmGE9ZmZWQpZBcBiwpmC4LR1XzhXA/FITJE2S1CKpZf369VUs0czMsgwClRgXJRtKZ5EEwQ2lpkfE9IhoioimhoaGKpZoZmZZdkPdBgwqGG4E1hY3kjQCuAc4LyI2ZliPmZmVkOUawSJgmKShknoD44F5hQ0kDQbmApdExMoMazEzszIyWyOIiG2SpgCPAz2AGRHRKmlyOn0acBPQD/iuJIBtEdGUVU1mZrazTK9QFhHNQHPRuGkF968ErsyyBjMz65jPLDYzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7Ocy7T30bw6+fpZnZ538e0Tq1iJmdmueY3AzCznvEbQxbx1y6c7Pe/gm16rYiVmlhdeIzAzy7lMg0DSGEkrJK2WNLXEdEm6M53+qqSTsqzHzMx2ltmmIUk9gLuBzwFtwCJJ8yJiWUGz84Bh6e0U4HvpX6sy78A2s3Ky3EcwGlgdEa8DSJoDnA8UBsH5wKyICGChpIMkDYiIdzKsy7qQzgZUluHk0LS8UfIdnMEDSxcAY9IL1CPpEuCUiJhS0OYx4P9FxL+nw08BN0RES9FjTQImpYNHAyuqVGZ/YEOVHqtaXFNlumJN0DXrck2V2dtrOjwiGkpNyHKNQCXGFadOJW2IiOnA9GoUtcOTSy0R0VTtx90TrqkyXbEm6Jp1uabK5LmmLHcWtwGDCoYbgbWdaGNmZhnKMggWAcMkDZXUGxgPzCtqMw+YmB49dCrw394/YGZWW5ltGoqIbZKmAI8DPYAZEdEqaXI6fRrQDIwFVgO/Ab6SVT1lVH1zUxW4psp0xZqga9blmiqT25oy21lsZmbdg88sNjPLOQeBmVnO5SYIJL0p6TVJSyS1pOMOkfQTSavSvwdnXMMgSc9IWi6pVdK16fibJb2d1rZE0tiCeb6WdsGxQtK5WdaXPl8fSS9LeiWt8esF065J62iV9A9Z11JU17WSlqbPfV06rqbvX/qcPST9PD0HBkknSFrYvlxJGl3QNpP3TtIMSeskLS0YV/J/Ielzkhany/5iSWeXeLx5hY9VhfpKLkOSbpf0n2l3Mg9JOqhgnlov5+U+i7X+Tji64HO/RNJ7Bcv3Tp83Sb0k/SB9P5dL+lpVComIXNyAN4H+ReP+AZia3p8KfCPjGgYAJ6X39wdWAscCNwN/U6L9scArwD7AUOAXQI+MaxSwX3q/F/AScCpwFvAksE867dAavnfHA0uBviQHODxJ0i1JTd+/9Hn+F/BvwGPp8BPAeen9scCzWb93wOnAScDSXS3LwInAwIL/49tFj/XF9PUsrUZtu1iG/hjomY7/RkGN9VjOy30Wa75MFdTUA/j/wOHlPm/AxcCc9H5fku+1IXv63LlZIyjjfOAH6f0fAH+W5ZNFxDsR8bP0/mZgOXDYLuqbExG/i4g3SI6uGt1B+2rUGBHx63SwV3oL4CqSs8B/l7Zbl2UdRT4FLIyI30TENuA54AvU+P2T1Ah8HrinYHQAB6T3D+Tj82Aye+8i4nlgU9Hokv+LiPh5RLTX1Ar0kbRP+nr2Iwm2/1uNugrqK7kMRcQT6fsHsJDkvKH22mu9nJf7LNZ0mSpyDvCLiPgvyn/eAvg9ST2BfYEtwHt7+sR5CoIAnkhXj9u7q/hkpOctpH8PrVUxkoaQ/Fp7KR01JV1lnlGwOnoYsKZgtjY6Do5q1dZD0hJgHfCTiHgJGA78oaSXJD0naVTWdRRYCpwuqZ+kviS/vAdR+/fv28D/AT4sGHcdcLukNcAdQPuqeq3fu0r+F38O/Lz9ywW4FfgmyaHbVVVmGSp0OTA/vV+X5bxd0Wexbt8JJOdazU7vl/u8PQi8D7wDvAXcERHFPwp2W56C4A8i4iSSHk+vlnR6vQpJf4n9CLguIt4j6XX1SOAEkjf4m+1NS8ye+fG+EbE9Ik4g+cU2WtLxJJtkDiZZxb8eeEBSqfqyqGc5yaaEnwALSDYjbOtwpiqT9CfAuohYXDTpKuCvI2IQ8NfA99tnKfEwdTtWW9JxJP/Dr6bDJwBHRcRDWTxfmWWovZYbSd6/+9pHlXqILOoqVuKzWBdKTrodB/wwHVXu8zYa2A4MJNmM9r8lHbGnz5+bIGhfPU5XsR4i+Yf+UtIAgPRv5ps7JPUiWfDui4i5aU2/TD84HwL/zMerxXXtgiMi3gWeBcaktcxNV/tfJvlV3L+GtXw/Ik6KiNNJNousorbv3x8A4yS9CcwBzpb0r8ClwNy0zQ+p33tX9n+RbtJ6CJgYEb9IR38GODl9Pf8ODJf0bLWLKlqGkHQp8CfAlyLd0E2dlvNSn0Xq8J2QOg/4WUT8Mh0u93m7GFgQEVvT77IXgD3uiygXQSDp9yTt336fZKfVUpIuLi5Nm10KPJJxHSL5xbg8Ir5VMH5AQbMvpLWR1jde0j6ShpLsIH054xob2o/mkLQv8EfAfwIPA2en44cDvalhT42SDk3/DibZwTmbGr5/EfG1iGiMiCEkq/BPR8SXSb6wzkibnU0SUFD7967k/yJ9L38MfC0iXih4Pd+LiIHp6/kssDIizqxGIeWWIUljgBuAcRFRuDmqHst5yc8iNf5OKDCBjzcLQfnP21skP0KUfpedSvL53DN7ure5O9yAI0g2J7xCssPsxnR8P+Apkg/vU8AhGdfxWZJV3leBJeltLPAvwGvp+HnAgIJ5biQ5imIF6dEpGdc4Avh5WstS4KZ0fG/gX9NxPwPOrvF7+FOSa1m8ApxTj/evoJYz+fiooc8Ci9O6XgJOzvq9I/nCeAfYSvLL8Ypy/wvgb0m2KS8puB1a9HhDqO5RQ+WWodUk+wLa65hWx+W83Gex5ssUydE/G4EDC8aV/LwB+5Gsebamn4frq1GDu5gwM8u5XGwaMjOz8hwEZmY55yAwM8s5B4GZWc45CMzMcs5BYFYhSb8vaY6kX0haJqlZ0nBJCyS9q7RH0oL2knSbpJVpT5F/Va/azTqS2aUqzfYm6QlIDwE/iIjx6bgTgE8Ct5McC/7VotkuIzlj9piI+LD9pDizrsZBYFaZs4CtkVxrG4CIWNJ+X9KZJea5Crg4kq5DiNr22GpWMW8aMqvM8SRnEO+OI4GLlFywZr6kYRnUZbbHHARm2dkH+CAimkg6E5xR53rMSnIQmFWmFTh5N+dpI+ndEpL9CyOqWpFZlTgIzCrzNLCPpL9sHyFplKQzOpjnYdIeJEl6KF2ZXXlmnedO58wqJGkgyVXKTgY+ILle7HUkm3yOIekZciNwRUQ8nnbFfB8wGPg1MDkiXql13Wa74iAwM8s5bxoyM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOf+B4IkTGCvWjN6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confirm the data value status of all features\n",
    "for i in df.columns:\n",
    "    sns.countplot(x = i, hue = \"click\", data = df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the chart listed above, it is obvious that ['device_id','C14','C17','C19','C20','C21'] these features have only one value, which is not for the prediction model Meaning, so move these features out of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['device_id', 'C14', 'C17', 'C19', 'C20', 'C21'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding for all variables\n",
    "df = pd.get_dummies(df)\n",
    "\n",
    "# According to the processed df data table, split train and test again\n",
    "train = df[:train_len]\n",
    "test = df[train_len:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Export the processed train and test data sets to avoid lengthy processing time each time.\n",
    "\n",
    "# train.to_csv('new_train.csv', index=False)\n",
    "# test.to_csv('new_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Read the processed train, test data set, skip the lengthy re-execution processing time.\n",
    "# train = pd.read_csv('new_train.csv')\n",
    "# test = pd.read_csv('new_test.csv')\n",
    "# train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the data set is very large, and the proportion of positive labels only accounts for about 17% of all data, the proportion is obviously out of balance, and an algorithm with enhanced weighting function is required. Therefore, it is decided to use the xgboost algorithm to solve the problem of strengthening the weight, and at the same time use the GPU to effectively save the computing time.\n",
    "\n",
    "In order to save time in tuning parameters, before implementing the prediction model, 100 decision trees will be established, the best parameters and important features will be found by Grid Search, and finally the model will be established by the xgboost algorithm. In order to shorten the construction time of the decision tree, the negative label data will be sampled, and all the positive label data will be combined into a 50% data set to balance the weight problem. At the same time, because the ratio of positive and negative labels is balanced, ROC_AUC score will be used for parameter adjustment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the train data set, from the data with the label 0, randomly sample as many as the data with the label 1, and combine them into a data set with 50% of the positive and negative labels.\n",
    "pre_X = train[train['click'] == 0].sample(n=len(train[train['click'] == 1]), random_state=111)\n",
    "pre_X = pd.concat([pre_X, train[train['click'] == 1]]).sample(frac=1)\n",
    "pre_y = pre_X[['click']]\n",
    "pre_X.drop(['click'], axis=1, inplace=True)\n",
    "test.drop(['click'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the new data set into training set and validation set\n",
    "pre_X_train, pre_X_test, pre_y_train, pre_y_test = train_test_split(pre_X, pre_y, test_size=0.20, stratify=pre_y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 100 folds for each of 38 candidates, totalling 3800 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:   27.9s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 426 tasks      | elapsed: 10.5min\n",
      "[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed: 28.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1226 tasks      | elapsed: 56.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1776 tasks      | elapsed: 97.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2426 tasks      | elapsed: 122.8min\n",
      "[Parallel(n_jobs=-1)]: Done 3176 tasks      | elapsed: 171.8min\n",
      "[Parallel(n_jobs=-1)]: Done 3800 out of 3800 | elapsed: 227.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7310717446006411,\n",
       " DecisionTreeClassifier(criterion='entropy', max_depth=10),\n",
       " {'criterion': 'entropy', 'max_depth': 10})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute Grid Search to adjust the parameters and create 100 trees to obtain the best parameters\n",
    "params = {\"criterion\":[\"gini\", \"entropy\"], \"max_depth\":range(1,20)}\n",
    "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid=params, scoring='roc_auc', cv=100, verbose=1, n_jobs=-1)\n",
    "grid_search.fit(pre_X_train, pre_y_train)\n",
    "grid_search.best_score_, grid_search.best_estimator_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>site_id_very_low</th>\n",
       "      <td>0.415879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_id_very_high</th>\n",
       "      <td>0.170780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_id_very_high</th>\n",
       "      <td>0.077292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_id_very_low</th>\n",
       "      <td>0.065363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_category_higher</th>\n",
       "      <td>0.065242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C15_728</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>C15_480</th>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "site_id_very_low     0.415879\n",
       "app_id_very_high     0.170780\n",
       "site_id_very_high    0.077292\n",
       "app_id_very_low      0.065363\n",
       "app_category_higher  0.065242\n",
       "...                       ...\n",
       "C15_728              0.000000\n",
       "C15_480              0.000000\n",
       "C15_120              0.000000\n",
       "C15_1024             0.000000\n",
       "C15_768              0.000000\n",
       "\n",
       "[103 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a decision tree model based on the results of Grid Search and fit the complete data (pre-data)\n",
    "tree = grid_search.best_estimator_\n",
    "tree.fit(pre_X,pre_y)\n",
    "\n",
    "# Output important features and sort them according to their importance\n",
    "feature_importances = pd.DataFrame(tree.feature_importances_)\n",
    "feature_importances.index = pre_X_train.columns\n",
    "feature_importances = feature_importances.sort_values(0,ascending=False)\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjust the pre-work training set and validation set, reduce the feature importance to 1/3 of the importance ranking based on the feature importance\n",
    "pre_X_train = pre_X_train[feature_importances.index[:int(len(feature_importances)/3)]]\n",
    "pre_X_test = pre_X_test[feature_importances.index[:int(len(feature_importances)/3)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 100 folds for each of 22 candidates, totalling 2200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    7.3s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:   53.9s\n",
      "[Parallel(n_jobs=-1)]: Done 426 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed:  9.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1226 tasks      | elapsed: 16.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1776 tasks      | elapsed: 23.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2200 out of 2200 | elapsed: 32.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7310614180888089,\n",
       " DecisionTreeClassifier(criterion='entropy', max_depth=11),\n",
       " {'criterion': 'entropy', 'max_depth': 11})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use 33% of the important features to re-adjust the Grid Search parameters\n",
    "params = {\"criterion\":[\"gini\", \"entropy\"], \"max_depth\":range(1,12)}\n",
    "grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid=params, scoring='roc_auc', cv=100, verbose=1, n_jobs=-1)\n",
    "grid_search.fit(pre_X_train, pre_y_train)\n",
    "grid_search.best_score_, grid_search.best_estimator_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>site_id_very_low</th>\n",
       "      <td>0.411523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_id_very_high</th>\n",
       "      <td>0.168991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_id_very_high</th>\n",
       "      <td>0.076482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_category_higher</th>\n",
       "      <td>0.065132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_id_very_low</th>\n",
       "      <td>0.064678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C16_250</th>\n",
       "      <td>0.030381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18_1</th>\n",
       "      <td>0.023899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_model_very_high</th>\n",
       "      <td>0.019590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_model_very_low</th>\n",
       "      <td>0.014992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_model_lower</th>\n",
       "      <td>0.012921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_domain_very_low</th>\n",
       "      <td>0.012916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_category_higher</th>\n",
       "      <td>0.010432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_conn_type_0</th>\n",
       "      <td>0.008111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_id_higher</th>\n",
       "      <td>0.006984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C16_50</th>\n",
       "      <td>0.006497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18_2</th>\n",
       "      <td>0.006142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banner_pos_0</th>\n",
       "      <td>0.005621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_19-21</th>\n",
       "      <td>0.005569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18_3</th>\n",
       "      <td>0.005009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_17-19</th>\n",
       "      <td>0.004916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banner_pos_1</th>\n",
       "      <td>0.004379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday_3</th>\n",
       "      <td>0.003426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_ip_very_high</th>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_ip_very_low</th>\n",
       "      <td>0.003274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday_2</th>\n",
       "      <td>0.003237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_21-23</th>\n",
       "      <td>0.002959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday_1</th>\n",
       "      <td>0.002618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_ip_mid</th>\n",
       "      <td>0.002570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_id_lower</th>\n",
       "      <td>0.002507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_category_very_high</th>\n",
       "      <td>0.002465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_domain_very_low</th>\n",
       "      <td>0.002368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_domain_lower</th>\n",
       "      <td>0.002208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C15_300</th>\n",
       "      <td>0.002040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_domain_very_high</th>\n",
       "      <td>0.001829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0\n",
       "site_id_very_low         0.411523\n",
       "app_id_very_high         0.168991\n",
       "site_id_very_high        0.076482\n",
       "app_category_higher      0.065132\n",
       "app_id_very_low          0.064678\n",
       "C16_250                  0.030381\n",
       "C18_1                    0.023899\n",
       "device_model_very_high   0.019590\n",
       "device_model_very_low    0.014992\n",
       "device_model_lower       0.012921\n",
       "site_domain_very_low     0.012916\n",
       "site_category_higher     0.010432\n",
       "device_conn_type_0       0.008111\n",
       "site_id_higher           0.006984\n",
       "C16_50                   0.006497\n",
       "C18_2                    0.006142\n",
       "banner_pos_0             0.005621\n",
       "hour_19-21               0.005569\n",
       "C18_3                    0.005009\n",
       "hour_17-19               0.004916\n",
       "banner_pos_1             0.004379\n",
       "weekday_3                0.003426\n",
       "device_ip_very_high      0.003333\n",
       "device_ip_very_low       0.003274\n",
       "weekday_2                0.003237\n",
       "hour_21-23               0.002959\n",
       "weekday_1                0.002618\n",
       "device_ip_mid            0.002570\n",
       "app_id_lower             0.002507\n",
       "site_category_very_high  0.002465\n",
       "app_domain_very_low      0.002368\n",
       "app_domain_lower         0.002208\n",
       "C15_300                  0.002040\n",
       "app_domain_very_high     0.001829"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adjust the complete data set of the pre-work, and reduce the importance of features to 1/3 of the importance ranking\n",
    "pre_X = pre_X[feature_importances.index[:int(len(feature_importances)/3)]]\n",
    "\n",
    "# Create a decision tree model based on the results of Grid Search and fit the complete data (pre-data)\n",
    "tree = grid_search.best_estimator_\n",
    "tree.fit(pre_X,pre_y)\n",
    "\n",
    "# Output important features and sort them according to their importance\n",
    "feature_importances = pd.DataFrame(tree.feature_importances_)\n",
    "feature_importances.index = pre_X_train.columns\n",
    "feature_importances = feature_importances.sort_values(0,ascending=False)\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The features of the final prediction model will use variables with feature values above .005\n",
    "feature_len = len(feature_importances[feature_importances[feature_importances.columns[0]] > 0.005])\n",
    "\n",
    "# Adjust the characteristics of the final complete Train Set and Test Set\n",
    "y = train[['click']]\n",
    "X = train[feature_importances[:feature_len].index]\n",
    "\n",
    "\n",
    "test = test[feature_importances[:feature_len].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1676155    1499]\n",
      " [ 340479    3315]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI for Logistic Regression: 3.227684356939682 \n",
      "Evaluating classifier: Logistic Regression\n",
      "Precision: 0.8069210892349205: Recall: 0.8308252302310027, F-beta score: 0.7215674882420191, AUC of ROC curve: 0.5043744462545144\n",
      "[[1668780    8874]\n",
      " [ 330225   13569]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI for Decision Tree: 3.1826131572446883 \n",
      "Evaluating classifier: Decision Tree\n",
      "Precision: 0.7956532893111721: Recall: 0.8322494568250086, F-beta score: 0.7424557541620158, AUC of ROC curve: 0.5170894366414632\n",
      "[[1668743    8911]\n",
      " [ 330189   13605]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI for Random Forest: 3.1824071074612386 \n",
      "Evaluating classifier: Random Forest\n",
      "Precision: 0.7956017768653096: Recall: 0.8322489621301167, F-beta score: 0.7425141392380941, AUC of ROC curve: 0.5171307662725355\n",
      "[[1672354    5300]\n",
      " [ 335278    8516]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI for Multi-Layer Perceptron: 3.184633939308679 \n",
      "Evaluating classifier: Multi-Layer Perceptron\n",
      "Precision: 0.7961584848271698: Recall: 0.8315178030797725, F-beta score: 0.7329726764425013, AUC of ROC curve: 0.5108057370370414\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gokul\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:24:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[[1668738    8916]\n",
      " [ 330184   13610]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI for XGBClassifier: 3.182381188025783 \n",
      "Evaluating classifier: XGBClassifier\n",
      "Precision: 0.7955952970064457: Recall: 0.8322489621301167, F-beta score: 0.742522295319026, AUC of ROC curve: 0.5171365478928557\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import fbeta_score\n",
    "\n",
    "# Create list of classifiers\n",
    "names = ['Logistic Regression',  'Decision Tree',\n",
    "         'Random Forest', 'Multi-Layer Perceptron', 'XGBClassifier']\n",
    "clfs = [LogisticRegression(), \n",
    "        DecisionTreeClassifier(), RandomForestClassifier(), \n",
    "        MLPClassifier(hidden_layer_sizes = (5, ), max_iter = 50),\n",
    "        XGBClassifier(tree_method = 'hist', n_jobs=-1, n_estimators=500, max_depth=11)]\n",
    "\n",
    "# Produce a classification report for all classifiers\n",
    "for name, classifier in zip(names, clfs):\n",
    "      classifier.fit(X,y.values.ravel())\n",
    "      y_pred = classifier.predict(X)\n",
    "      prec = precision_score(y,y_pred, average = 'weighted')\n",
    "      conf_matrix = confusion_matrix(y,y_pred, labels=[0, 1])\n",
    "      r, cost = 0.2, 0.05 \n",
    "      roi = prec * r / cost\n",
    "      print(conf_matrix)\n",
    "# Draw the confusion matrix and view the prediction results\n",
    "\n",
    "      fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
    "      ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n",
    "      for i in range(conf_matrix.shape[0]):\n",
    "         for j in range(conf_matrix.shape[1]):\n",
    "            ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center')\n",
    "\n",
    "      plt.xlabel('Predicted label')\n",
    "      plt.ylabel('True label')\n",
    "\n",
    "      plt.tight_layout()\n",
    "      plt.show()\n",
    "      print(\"ROI for %s: %s \" %(name, roi))\n",
    "    \n",
    "      print(\"Evaluating classifier: %s\" %(name))\n",
    "      y_score = classifier.predict_proba(X)\n",
    "      recall = recall_score(y,y_pred, average = 'weighted')\n",
    "      fbeta = fbeta_score(y,y_pred, beta = 0.5, average = 'weighted')\n",
    "      roc_auc = roc_auc_score(y,y_pred)\n",
    "      print(\"Precision: %s: Recall: %s, F-beta score: %s, AUC of ROC curve: %s\" %(prec, recall, fbeta, roc_auc))\n",
    "      \n",
    "\n",
    "# Use xgboost to model, and specify the node depth limit obtained from the previous tuning. Use xgboost to model and specify the node depth limit obtained from the previous tuning\n",
    "# model = XGBClassifier(tree_method = 'hist', n_jobs=-1, n_estimators=500, max_depth=11)\n",
    "# model.fit(X,y.values.ravel())\n",
    "# y_pred = model.predict(X)\n",
    "# print(\"Roc_auc_score: \",roc_auc_score(y,y_pred)*100,\"%\")\n",
    "\n",
    "# # Draw the confusion matrix and view the prediction results\n",
    "# confmat = confusion_matrix(y_true=y, y_pred=y_pred, labels=[0, 1])\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
    "# ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)\n",
    "# for i in range(confmat.shape[0]):\n",
    "#     for j in range(confmat.shape[1]):\n",
    "#         ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')\n",
    "\n",
    "# plt.xlabel('Predicted label')\n",
    "# plt.ylabel('True label')\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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